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Instructor

Peter Chen

Peter Chen is an analytics and data science professional that has an eclectic and deep background. He has previously worked in various senior positions at companies such as Algebraix Data, Petco, Mitchell International, Sempra Energy ,etc. He has been profiled in various media articles (“A Confession of a Data Miner”) about his analytics and data mining experience. He is widely published in industry trade magazines about analytics & data science. Peter received his BS in Management Science from the Massachusetts Institute of Technology/Sloan School of Management, his Masters in General Management from Harvard and is currently working on his second Masters in Software Engineering/Data Science at Harvard.

Course Description

My name is Peter Chen and I am the instructor for this course. I want to introduce you to the wonderful world of Unsupervised Machine Learning. Specifically, we will focus on Clustering algorithms and methods through practical examples and code. More importantly, it will get you up and running quickly with a clear conceptual understanding. The course has code & sample data for you to run and learn from. It also encourages you to explore your own datasets using Clustering algorithms.
Prerequisites:
Beginner knowledge of Python. It's used mostly for expository reasons. You do not need to be a Python expert. Basic math and comfortable with basic probability and statistics.

* Apply Python code to their data sets to solve clustering various problems

* Evaluate the quality of clustering using Silhouette plots

* Learn about different industry applications of Clustering

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Basic Python. Do not need to be an expert programmer. We use Python mainly for expository reasons. Basic probability math.

Who should take this course? Who should not?

Students who are interested in a practical introduction to clustering, a kind of unsupervised machine learning. Want an intuitive understanding of the theory behind clustering.

Students can use these methods and algorithms for hot applications such as marketing analytics, customer segmentation, anomaly detection, fraud detection, and other practical applications in their respective fields. Must like to play with data and code.

Curriculum

Module 1: Welcome & Introductions

Lecture 1
Welcome to the Course

Lecture 2
Course Overview and Introductions

Module 2: K-Means Clustering

Lecture 3
K-Means Clustering

Lecture 4
How does K-means do that?

Lecture 5
Similarity Measures

Lecture 6
Issues with K-Means

Module 3: Gaussian Mixture Models

Lecture 7
GMM Introductions

Lecture 8
GMM: Code Examples

Lecture 9
GMM as Density Estimators

Lecture 10
GMM: Optimal Number of Components

Lecture 11
GMM - Generate New Data

Module 4: Hierarchical Clustering

Lecture 12
Introductions to Hierarchical Clustering

Lecture 13
Linkage Methods

Lecture 14
Hierarchical Clustering Walk-Through

Lecture 15
Divisive Algorithm

Lecture 16
Hierarchical Clustering - Code Examples

Module 5: Methods for Selecting Number of Clusters

Lecture 17
Methods for Selecting Number of Clusters

Module 6: Evaluating the Quality of the Clustering

Lecture 18
Evaluating the Quality of Clustering

Module 7: Industry Applications

Lecture 19
Industry Applications

Module 8: Mini-Project: Pulling It All Together

Lecture 20
Mini-Project

Module 9: Mini-Project Solution Preview

Lecture 21
Solution Preview

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